Contributors to the 2024 IMIA Yearbook of Medical Informatics
Bibliographic record
Abstract
Editor in chief: Lina F. Soualmia Normandie University, UNIROUEN, LITIS Rouen, France Lina.Soualmia@litislab.fr Editors: Kate Fultz Hollis Oregon Health & Science University Portland, OR, USA fultzhol@ohsu.edu Fleur Mougin University of Bordeaux & BPH UMR_S 1219 Bordeaux, France Fleur.Mougin@u-bordeaux.fr Editorial Assistant: Yann Rousselot Freelance Paris, France yearbook@imia-services.org Best Papers Editor: Adrien Ugon ESIEE Paris & LIP6 UMR_7606 adrien.ugon@esiee.fr Advisory Board: Reinhold Haux Peter L. Reichertz Institute for Medical Informatics TU Braunschweig and Hannover Medical School Braunschweig, Germany reinhold.haux@plri.de Victor Maojo Universidad Politécnica de Madrid Departamento de Inteligencia Artificial Facultad de Informatica Madrid, Spain vmaojo@fi.upm.es George Mihalas University of Medicine and Pharmacy Dept. of Medical Informatics Timisoara, Romania mihalas@EFMI.info Section Editors: Sanjay Aneja Yale School of Medicine, New Haven, CT, USA sanjay.aneja@yale.edu Christian Baumgartner Graz University of Technology Graz, Austria christian.baumgartner@tugraz.at Mary Lauren Benton Baylor University Waco, TX, USA marylauren_benton@baylor.edu Meryl Bloomrosen American Medical Association Chicago, Il, USA meryl.bloomrosen@ama-assn.org Georgeta Bordea University of Bordeaux & BPH UMR_S 1219 Bordeaux, France georgeta.bordea@u-bordeaux.fr Jean Charlet DRCI/AP-HP & LIMICS UMR_S 1142 Paris, France jean.charlet@sorbonne-universite.fr Licong Cui The University of Texas Health Science Center at Houston Houston, TX, USA licong.cui@uth.tmc.edu Thomas M. Deserno Peter L. Reichertz Institute for Medical Informatics TU Braunschweig and Hannover Medical School Braunschweig, Germany thomas.deserno@plri.de Gayo Diallo University of Bordeaux & BPH UMR_S 1219 Bordeaux, France gayo.diallo@u-bordeaux.fr Brian E. Dixon Department of Health Policy and Management, Richard M. Fairbanks School of Public Health, Indiana University, Indianapolis, IN, USA bedixon@regenstrief.org Sue S. Feldman University of Alabama at Birmingham, Birmingham, AL, USA sfeldman@uab.edu Natalia Grabar CNRS, Univ. Lille, UMR 8163 STL - Savoirs Textes Langage Lille, France natalia.grabar@univ-lille.fr Cyril Grouin Université Paris-Saclay, CNRS, LISN Orsay, France cyril.grouin@lisn.upsaclay.fr Werner Hackl UMIT TIROL Private University for Health Sciences and Health Technology Hall in Tirol, Austria werner.hackl@umit-tirol.at John H. Holmes Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman, School of Medicine, Philadelphia, PA, USA jhholmes@pennmedicine.upenn.edu Dipak Kalra The University of Gent Gent, Belgium dipak.kalra@i-hd.eu Annie Y. S. Lau Centre for Health Informatics Australian Institute of Health Innovation Macquarie University, Australia annie.lau@mq.edu.au Christoph U. Lehmann Clinical Informatics Center University of Texas SW Medical Center Dallas, Texas, USA Christoph.Lehmann@utsouthwestern.edu Scott McGrath University of California Berkeley Missoula, USA smcgrath@berkeley.edu Sabrina B. Neururer UMIT TIROL - Private University for Health Sciences and Health Technology Hall in Tirol, Austria sabrina.neururer@umit-tirol.at Ravi B. Parikh Abramson Cancer Center, Department of Medicine, Philadelphia, PA, USA Ravi.Parikh@pennmedicine.upenn.edu Bernhard Pfeifer Tyrolean Federal Institute for Integrated Care Tirol Kliniken GmbH Innsbruck, Austria bernhard.pfeifer@tirol-kliniken.at Leticia Rittner MICLab University of Campinas Campinas, Brazil lrittner@unicamp.br Cécilia Samieri University of Bordeaux & BPH UMR_S 1219 Bordeaux, France cecilia.samieri@u-bordeaux.fr Yalini Senathirajah University of Pittsburgh School of Medicine Pittsburgh, USA yalini@pitt.edu Anthony Solomonides NorthShore University HealthSystem Evanston, Illinois, USA tony.solomonides@gmail.com Pascal Staccini IRIS Department, Lab RETINES Faculté de Médecine, Université Côte d'Azur Nice, France pascal.staccini@univ-cotedazur.fr Vignesh Subbian College of Engineering The University of Arizona Tucson, Arizona, USA vsubbian@arizona.edu Xavier Tannier Sorbonne Université Université Sorbonne Paris Nord, INSERM, LIMICS Paris, France xavier.tannier@sorbonne-universite.fr Regional Editors: Naoki Nakashima APAMI nnaoki@med.kyushu-u.ac.jp Ghislain B. Kouematchoua Tchuitcheu HELINA gkouema@gmail.com Marcelo Silva IMIA-LAC marcelo.silva@sbis.org.br Najeeb Al-Shorbaji MENAHIA shorbajin@gmail.com James J. Cimino AMIA Birmingham, AL, USA ciminojj@gmail.com Andre Kushniruk Digital Health Canada: Canada's Health Informatics Association Toronto, ON, Canada andrek@uvic.ca Lăcramioara Stoicu-Tivadar EFMI lacramioara.stoicu-tivadar@upt.ro IMIA Executive Director: Elaine Huesing imia@imia-services.org Publication History Article published online: 08 April 2025 © 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/) Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.218 | 0.272 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".